Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916779755831296 |
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| author | Dombrowski, Mischa Prenner, Andrea Kainz, Bernhard |
| author_facet | Dombrowski, Mischa Prenner, Andrea Kainz, Bernhard |
| contents | Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workings regarding bias encoding and detection of data drift for disease classification models. Additionally, to evaluate the severity in case of a significant drift, we provide an overview of the methods developed for classifier accuracy estimation in case of no access to ground truth labels. This should enable practitioners to implement methods ensuring reliable ML deployment and consistent prediction performance over time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_17800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey Dombrowski, Mischa Prenner, Andrea Kainz, Bernhard Human-Computer Interaction Image and Video Processing Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workings regarding bias encoding and detection of data drift for disease classification models. Additionally, to evaluate the severity in case of a significant drift, we provide an overview of the methods developed for classifier accuracy estimation in case of no access to ground truth labels. This should enable practitioners to implement methods ensuring reliable ML deployment and consistent prediction performance over time. |
| title | Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey |
| topic | Human-Computer Interaction Image and Video Processing |
| url | https://arxiv.org/abs/2409.17800 |